Teaching simulations: no live AI. Progress stays on this device; no entered text is saved or sent.
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Which word comes next?
Several, each with its own probability: a language model assigns probabilities to upcoming tokens. Here, three words and illustrative weights make that mechanism visible.
Change the context and the temperature, then sample a word.
More concentrated
More varied
Temperature reshapes the probabilities. It measures neither truth nor intelligence.
Which word will come out?
Possible futuresIllustrative probabilities
Your samples
A likely continuation is not evidence. The same machine can produce a fluent sentence and a convincing mistake. Try two contexts and two temperatures, then sample a word.
Does the source actually say that?
Not always. Read the fictional source document, then classify each claim in the summary. Missing information is not automatically false.
Fictional document
Meeting notes, Riverside Library
The team proposes a 90-minute workshop to explore AI. The room can hold 24 people. The date is yet to be confirmed. Free registration is being considered, but the budget has not yet been approved.
Your only source for this experiment.
Summary to check
Return to the source. Distinguish “supported”, “contradicted” and “not stated”. This is the reasoning we practise together in a workshop.
What makes an instruction useful?
A request someone can actually follow. Turn “tell me about AI” into one: build, compare and copy your instruction.
Use a fictional example. This text stays on the page.
What the request containsBefore
“Tell me about AI.”
Your instruction, assembled as you go
This page only assembles your instruction: no AI generates a response.
A precise instruction makes the result easier to evaluate. It does not guarantee accuracy. Choose your audience, task and at least two criteria to finish this experiment.
Three experiments. And with your group?
You explored prediction, checked claims and built an instruction. Imagine the conversations this can spark with your group.